MM Pytorch API: TabularTransform for input tabular sequence

[sararb]

Fixes # (issue)

Goals :soccer:

• Add support for padding, transforming, and masking sequential inputs data in MM Pytorch backend
• The implemented transform classes should: - Support multiple targets - Be used for training, evaluation, and inference

Implementation Details :construction:

• [x] Implement TabularBatchPadding to pad a group of sequential inputs
• [x] Implement TabularPredictNext for generating targets of causal next item prediction
• [ ] Implement TabularPredictLast for generating targets of last item prediction
• [ ] Implement TabularPredictRandom for generating targets of predicting one random item and truncate the sequence so that the random item is at the last position.
• [x] Implement TabularMaskRandom for masked language modeling training (MLM) strategy
• [x] Implement TabularMaskLast for masking last item in the sequence, generally used to evaluate models trained with MLM.

Testing Details :mag:

• Defined tests for padding and the different sequence transformations

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Documentation preview

https://nvidia-merlin.github.io/models/review/pr-1161